Jim Dowling – författare

Get up to speed on a new unified approach to building machine learning (ML) systems with batch data, real-time data, and large language models (LLMs) based on independent, modular ML pipelines and a shared data layer. With this practical book, data scientists and ML engineers will learn in detail how to develop, maintain, and operate modular ML systems.Author Jim Dowling introduces fundamental MLOps principles and practices for developing and operating reliable ML systems and describes the key data platform that you'll use to build and operate your ML systems: the feature store. Through examples, you'll look at how the feature store helps solve the hardest problem in ML-the data. When building systems, you'll move seamlessly from managing incremental datasets for training and fine-tuning to real-time data access and retrieval-augmented generation for online ML systems.With this book, you'll be able to:Make the leap from training ML models to building ML systemsDevelop an ML system as modular feature, training, and inference pipelinesDesign, develop, and operate batch ML systems, real-time ML systems, and fine-tuned LLM systems with retrieval-augmented generationLearn the problems a feature store for ML solves when building ML systemsUnderstand the principles of MLOps for developing and safely updating ML systemsJim Dowling is CEO of Hopsworks and an associate professor at KTH Royal Institute of Technology in Stockholm, Sweden.

Get up to speed on a new unified approach to building machine learning (ML) systems with a feature store. Using this practical book, data scientists and ML engineers will learn in detail how to develop and operate batch, real-time, and agentic ML systems.Author Jim Dowling introduces fundamental principles and practices for developing, testing, and operating ML and AI systems at scale. You'll see how any AI system can be decomposed into independent feature, training, and inference pipelines connected by a shared data layer. Through example ML systems, you'll tackle the hardest part of ML systems--the data, learning how to transform data into features and embeddings, and how to design a data model for AI.Develop batch ML systems at any scaleDevelop real-time ML systems by shifting left or shifting right feature computationDevelop agentic ML systems that use LLMs, tools, and retrieval-augmented generationUnderstand and apply MLOps principles when developing and operating ML systems

Get up to speed on a new unified approach to building machine learning (ML) systems with a feature store. Using this practical book, data scientists and ML engineers will learn in detail how to develop and operate batch, real-time, and agentic ML systems.Author Jim Dowling introduces fundamental principles and practices for developing, testing, and operating ML and AI systems at scale. You'll see how any AI system can be decomposed into independent feature, training, and inference pipelines connected by a shared data layer. Through example ML systems, you'll tackle the hardest part of ML systems--the data, learning how to transform data into features and embeddings, and how to design a data model for AI.Develop batch ML systems at any scaleDevelop real-time ML systems by shifting left or shifting right feature computationDevelop agentic ML systems that use LLMs, tools, and retrieval-augmented generationUnderstand and apply MLOps principles when developing and operating ML systems

This book constitutes the refereed proceedings of the 13th IFIP WG 6.1 International Conference on Distributed Applications and Interoperable Systems, DAIS 2013, held in Florence, Italy, in June 2013, as part of the 8th International Federated Conference on Distributed Computing Techniques, DisCoTec 2013. The 12 revised full papers and 9 short papers presented were carefully reviewed and selected from 42 submissions. The papers present state-of-the-art research results and case studies in the area of distributed applications and interoperable systems focussing on cloud computing, replicated storage, and peer-to-peer computing.